Die Kenntnis von Straßensperrungen ist wesentliche Randbedingung bei der privaten Disposition von Fahrten als auch der Entscheidungsfindung von Baulastträgern zu Verkehrslenkungsmaßnahmen. Zur Nutzbarmachung von Sperrinformationen bietet sich zuerst die Etablierung geeigneter Schnittstellen zum administrativen Prozess an. Dass dieser Weg noch nicht breitenwirksam Umsetzung gefunden hat, liegt oft im Aufwand bei der Anpassung komplexer Verwaltungsabläufe und -systeme begründet. Um trotzdem mit einer großen räumlichen Abdeckung und hoher Aktualität Sperrinformationen zu erschließen, wurde ein Ansatz entwickelt, welcher auf der Seite der verkehrlichen Wirkungen von Sperranordnungen ansetzt. Grundlage bilden die Daten von GNSS-basierten Floating Car Systemen. Zur Sperrungsermittlung wird die Befahrungshäufigkeit für Straßenabschnitte ausgewertet. Werden auf einem Abschnitt keine Fahrzeuge mehr erfasst, so wird von einer Sperrung ausgegangen. Dass die so gewonnene Sperraussage mit hoher Wahrscheinlichkeit mit der Realität übereinstimmt, sind geeignete Parametrierungen der Auswertung zu finden, welche die durch unterschiedliche Verkehrsnachfrage bedingte Befahrungshäufigkeiten der Straßenabschnitte berücksichtigt. Umgesetzt und getestet wurde der Ansatz im Kontext des Dresdner operativen Straßenverkehrsmanagementsystems VAMOS mit seiner Taxi-Floating Car-Komponente. Es konnte aufgezeigt werden, dass in dem für Verkehrsmanagementmaßnahmen relevanten Vorrangnetz mit einer Gesamtlänge von 540 Kilometern für 8 Prozent der Straßenabschnitte die Wirkung von Sperrungen in weniger als 6 Stunden, bei 59 % unter 24 Stunden und bei 79 % in weniger als 72 Stunden registriert werden können. Operative Reaktionen z. B. Warnhinweise auf Informationstafeln oder die Anpassung der kollektiven Zielführung an das spezifische Verkehrslagebild, erscheinen hier möglich. Natürlich können diese Informationen auch der Anreicherung etablierter Informationsflüsse, wie die Versorgung der Landesmeldestellen für den Verkehrswarndienst oder Routing-Dienste privater Service Provider, dienen.

The selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task which requires significant expert knowledge and experience. In this thesis we develop and investigate the application of an intelligent traffic control decision support system for road traffic management to assist the human operator to identify the most suitable control actions in order to deal with non-recurrent and non-predictable traffic congestion in a real-time situation. Our intelligent system employs a Fuzzy Neural Networks (FNN) Tool that combines the capabilities of fuzzy reasoning in measuring imprecise and dynamic factors and the capabilities of neural networks in terms of learning processes. In this work we present an effective learning approach with regard to the FNN-Tool, which consists of three stages: initializing the membership functions of both input and output variables by determining their centres and widths using self-organizing algorithms; employing an evolutionary Genetic Algorithm (GA) based learning method to identify the fuzzy rules; tune the derived structure and parameters using the back-propagation learning algorithm. We evaluate experimentally the performance and the prediction capability of this three-stage learning approach using well-known benchmark examples. Experimental results demonstrate the ability of the learning approach to identify all relevant fuzzy rules from the training data. A comparative analysis shows that the proposed learning approach has a higher degree of predictive capability than existing models. We also address the scalability issue of our intelligent traffic control decision support system by using a multi-agent based approach. The large network is divided into sub-networks, each of which has its own associated agent. Finally, our intelligent traffic control decision support system is applied to a number of road traffic case studies using the traffic network in Riyadh, in Saudi Arabia. The results obtained are promising and show that our intelligent traffic control decision support system can provide an effective support for real-time traffic control.

Net Neutrality implicates that all data being transmitted online is treated equal by Internet Service Providers. In 2016, the public debate regarding Net Neutrality in Sweden started growing as two major Mobile Network Operators were investigated by the Swedish Post and Telecom Authority for violation of European Union Net Neutrality regulations. Several studies have been conducted regarding Net Neutrality, most of them written in a legal, financial or technological perspective. This study takes another direction, aimed at understanding the consumer’s point of view regarding Net Neutrality. This study investigates whether or not consumers are aware of the subject and if so, how they value it. To measure this, an online survey was constructed, containing a total of 12 questions and statements. 77 people participated in the survey and out of these, 10 people participated in qualitative follow-up interviews. The interviews were semi-structured and individually designed according to each participant’s answers in the survey. This was done in order to gain a deeper understanding of the consumer’s reasoning while answering the survey. The results show that consumers lack knowledge regarding Net Neutrality. A major part of the consumers had not heard of the term or did not know the meaning of it, making it hard to determine whether or not the consumers value NN. However, when given a more concrete example of the implications of Internet Traffic Management from ISPs, the participants had a better understanding of what kind of implications NN could have on their Internet usage. They valued the implications of Net Neutrality, even though they did not know the theory of the term itself. The study also revealed that consumers have a big confidence in National Regulatory Authorities when it comes to looking after the openness of the Internet. Therefore, it is likely that National Regulatory Authorities must inform and educate consumers in the matter of Net Neutrality for them to value it and see its long-term implications. / Nätneutralitet innebär kortfattat att all data som skickas över Internet ska behandlas likvärdigt utav Internetleverantörer (ISP). Under 2016 växte debatten kring nätneutralitet i Sverige då två stycken mobiloperatörer utreddes utav Post- och Telestyrelsen. Båda dessa mobiloperatörer lanserade kampanjer till sina kunder som ansågs strida mot EU:s förordning 2015/2120 rörande nätneutralitet. Ett antal studier har redan gjorts på ämnet nätneutralitet, dock har de flesta haft en infallsvinkel där man tittat på juridiska, finansiella eller tekniska perspektiv. Den här studien har en annan infallsvinkel och riktar sig istället mot konsumenters syn på nätneutralitet. Den ämnar undersöka huruvida konsumenter känner till begreppet nätneutralitet och om de gör det, hur värderar de konceptet? För att undersöka detta konstruerades en online-enkät, innehållandes 12 frågor. 77 personer deltog i enkäten och utav dessa så deltog 10 personer i uppföljande, kvalitativa intervjuer. Intervjuerna var semi-strukturerade och individuella med frågor baserade på individens svar i enkäten. Dessa intervjuer var till för att ge en fördjupad förståelse av konsumenternas syn på nätneutralitet och deras resonemang kring svaren under enkäten. Resultaten visar att konsumenter, deltagande i den här studien, har låg kunskap kring nätneutralitet. Majoriteten utav deltagarna hade inte hört termen eller kände inte till dess mening, vilket gjorde det svårt att dra några slutsatser kring huruvida konsumenterna värderar konceptet. Men när konsumenterna fick ett mer konkret exempel på hur Internetleverantörers datahantering påverkar kundernas Internetanvändande så tycktes konsumenterna förstå vilka implikationer nätneutralitet kan ha på deras eget Internetanvändande. De tycktes således värdera innebörden av nätneutralitet, även om de inte förstod teorin kring konceptet. Studien påvisade också att konsumenter har en stor tilltro till vederbörande myndighet, Post- och Telestyrelsen här i Sverige, när det gäller att se efter Internets öppenhet och mångfald. Det är därför troligt att Post- och Telestyrelsen kommer att behöva informera och utbilda konsumenter rörande nätneutralitet för att få konsumenter att se värdet av och de långsiktiga implikationerna utav det.

Inom svenska järnvägen skapas en stor mängd information om hur objekt så som växlar och spårledningar beter sig, i form av dataloggar. Dessa används i dagsläget mycket sparsamt, trots sin stora potential till analys. Detta arbete ämnar utforma mjukvara som kan förädla dessa loggar och extrahera användbar information om banobjektens nuvarande och framtida tillstånd. Detta kan höja säkerheten och förebygga fel och på så vis minimera oplanerade driftstopp och kostsamma byten eller nödreparationer av utrustning. Arbetet resulterade i en mjukvara framtagen i Python som läser in loggar av godtycklig storlek och detekterar atypiska beteenden i ett flertal kategorier av banobjekt. Mjukvaran fungerar även som ett verktyg för mer användarvänlig hantering av dessa loggar, varur användaren kan sammanställa och presentera information som annars vore svårtillgänglig. / In the Swedish rail network, a large amount of information is generated regarding the behaviours of various objects such as switches and track circuits, which is then stored in logs. Currently, these logs are severely underused despite their great potential for analysis. The purpose of this project is to develop a software which can refine these logs and extract useful information about the current and future states of the objects. This can increase operational safety and prevent faults from occurring, thereby minimizing unplanned downtime and costly replacements or reparations of equipment. The project results in a software developed in Python which reads logs of an arbitrary size and detects atypical behaviours in several different categories of objects. The software also acts as a tool for more user-friendly handling of these logs, offering the ability to compile and present information which would otherwise be difficult to access.

We develop and evaluate two significant modeling concepts within the context of a large-scale Airspace Planning and Collaborative Decision-Making Model (APCDM) and, thereby, enhance its current functionality in support of both strategic and tactical level flight assessments. The first major concept is a new severe weather-modeling paradigm that can be used to assess existing tactical en route flight plan strategies such as the Flight Management System (FMS) as well as to provide rerouting strategies. The second major concept concerns modeling the mediated bartering of slot exchanges involving airline trade offers for arrival/departure slots at an arrival airport that is affected by the Ground Delay Program (GDP), while simultaneously considering issues related to sector workloads, airspace conflicts, as well as overall equity concerns among the airlines. This research effort is part of an $11.5B, 10-year, Federal Aviation Administration (FAA)-sponsored program to increase the U.S. National Airspace (NAS) capacity by 30 percent by the year 2010. Our innovative contributions of this research with respect to the severe weather rerouting include (a) the concept of "Probability-Nets" and the development of discretized representations of various weather phenomena that affect aviation operations; (b) the integration of readily accessible severe weather probabilities from existing weather forecast data provided by the National Weather Service (NWS); (c) the generation of flight plans that circumvent severe weather phenomena with specified probability levels, and (d) a probabilistic delay assessment methodology for evaluating planned flight routes that might encounter potentially disruptive weather along its trajectory. Given a fixed set of reporting stations from the CONUS Model Output Statistics (MOS), we begin by constructing weather-specific probability-nets that are dynamic with respect to time and space. Essential to the construction of the probability-nets are the point-by-point forecast probabilities associated with MOS reporting sites throughout the United States. Connections between the MOS reporting sites form the strands within the probability-nets, and are constructed based upon a user-defined adjacency threshold, which is defined as the maximum allowable great circle distance between any such pair of sites. When a flight plan traverses through a probability-net, we extract probability data corresponding to the points where the flight plan and the probability-net strand(s) intersect. The ability to quickly extract this trajectory-related probability data is critical to our weather-based rerouting concepts and the derived expected delay and related cost computations in support of the decision-making process. Next, we consider the superimposition of a flight-trajectory-grid network upon the probability-nets. Using the U.S. Navigational Aids (Navaids) as the network nodes, we develop an approach to generate flight plans that can circumvent severe weather phenomena with specified probability levels based on determining restricted, time-dependent shortest paths between the origin and destination airports. By generating alternative flight plans pertaining to specified threshold strand probabilities, we prescribe a methodology for computing appropriate expected weather delays and related disruption factors for inclusion within the APCDM model. We conclude our severe weather-modeling research by conducting an economic benefit analysis using a k-means clustering mechanism in concert with our delay assessment methodology in order to evaluate delay costs and system disruptions associated with variations in probability-net refinement-based information. As a flight passes through the probability-net(s), we can generate a probability-footprint that acts as a record of the strand intersections and the associated probabilities from origin to destination. A flight plan's probability-footprint will differ for each level of data refinement, from whence we construct route-dependent scenarios and, subsequently, compute expected weather delay costs for each scenario for comparative purposes. Our second major contribution is the development of a novel slot-exchange modeling concept within the APCDM model that incorporates various practical issues pertaining to the Ground Delay Program (GDP), a principal feature in the FAA's adoption of the Collaborative Decision-Making (CDM) paradigm. The key ideas introduced here include innovative model formulations and several new equity concepts that examine the impact of "at-least, at-most" trade offers on the entire mix of resulting flight plans from respective origins to destinations, while focusing on achieving defined measures of "fairness" with respect to the selected slot exchanges. The idea is to permit airlines to barter assigned slots at airports affected by the Ground Delay Program to their mutual advantage, with the FAA acting as a mediator, while being cognizant of the overall effect of the resulting mix of flight plans on air traffic control sector workloads, collision risk and safety, and equity considerations. We start by developing two separate slot-exchange approaches. The first consists of an external approach in which we formulate a model for generating a set of package-deals, where each package-deal represents a potential slot-exchange solution. These package-deals are then embedded within the APCDM model. We further tighten the model representation using maximal clique cover-based cuts that relate to the joint compatibility among the individual package-deals. The second approach significantly improves the overall model efficiency by automatically generating package-deals as required within the APCDM model itself. The model output prescribes a set of equitable flight plans based on admissible trades and exchanges of assigned slots, which are in addition conformant with sector workload capabilities and conflict risk restrictions. The net reduction in passenger-minutes of delay for each airline is the primary metric used to assess and compare model solutions. Appropriate constraints are included in the model to ensure that the generated slot exchanges induce nonnegative values of this realized net reduction for each airline. In keeping with the spirit of the FAA's CDM initiative, we next propose four alternative equity methods that are predicated on different specified performance ratios and related efficiency functions. These four methods respectively address equity with respect to slot-exchange-related measures such as total average delay, net delay savings, proportion of acceptable moves, and suitable value function realizations. For our computational experiments, we constructed several scenarios using real data obtained from the FAA based on the Enhanced Traffic Management System (ETMS) flight information pertaining to the Miami and Jacksonville Air Route Traffic Control Centers (ARTCC). Through our experimentation, we provide insights into the effect of the different proposed modeling concepts and study the sensitivity with respect to certain key parameters. In particular, we compare the alternative proposed equity formulations by evaluating their corresponding slot-exchange solutions with respect to the net reduction in passenger-minutes of delay for each airline. Additionally, we evaluate and compare the computational-effort performance, under both time limits and optimality thresholds, for each equity method in order to assess the efficiency of the model. The four slot-exchange-based equity formulations, in conjunction with the internal slot-exchange mechanisms, demonstrate significant net savings in computational effort ranging from 25% to 86% over the original APCDM model equity formulation. The model has been implemented using Microsoft Visual C++ and evaluated using a C++ interface with CPLEX 9.0. The overall results indicate that the proposed modeling concepts offer viable tools that can be used by the FAA in a timely fashion for both tactical purposes, as well as for exploring various strategic issues such as air traffic control policy evaluations; dynamic airspace resectorization strategies as a function of severe weather probabilities; and flight plan generation in response to various disruption scenarios. / Ph. D.

Improving road safety and optimizing the traffic flow – these are major challenges at urban intersections. In particular, strengthening the needs of vulnerable road users (VRUs) such as pedestrians, cyclists and e-scooter drivers is becoming increasingly important, combined with support for automated and connected driving. In the LTSA project, a new system is being developed and implemented exactly for this purpose. The LTSA is an intelligent infrastructure system that records the movements of all road users in the vicinity of an intersection using a combination of several locally installed sensors e.g. video, radar, lidar. AI-based software processes the detected data, interprets the movement patterns of road users and continuously analyzes the current traffic situation (digital twin). Potentially dangerous situations are identified, e.g. right turning vehicles and simultaneously crossing VRUs, and warning messages can be sent to connected road users via vehicle-to-infrastructure communication (V2X). Automated vehicles can thus adapt their driving maneuvers. In addition, the collected data is applied to improve traffic light control depending on the current traffic situation, especially for VRUs. This abstract describes the LTSA system and its implementation in the German city of Potsdam. The current project state is presented and an outlook on next steps is given.

The selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task which requires significant expert knowledge and experience. In this thesis we develop and investigate the application of an intelligent traffic control decision support system for road traffic management to assist the human operator to identify the most suitable control actions in order to deal with non-recurrent and non-predictable traffic congestion in a real-time situation. Our intelligent system employs a Fuzzy Neural Networks (FNN) Tool that combines the capabilities of fuzzy reasoning in measuring imprecise and dynamic factors and the capabilities of neural networks in terms of learning processes. In this work we present an effective learning approach with regard to the FNN-Tool, which consists of three stages: initializing the membership functions of both input and output variables by determining their centres and widths using self-organizing algorithms; employing an evolutionary Genetic Algorithm (GA) based learning method to identify the fuzzy rules; tune the derived structure and parameters using the back-propagation learning algorithm. We evaluate experimentally the performance and the prediction capability of this three-stage learning approach using well-known benchmark examples. Experimental results demonstrate the ability of the learning approach to identify all relevant fuzzy rules from the training data. A comparative analysis shows that the proposed learning approach has a higher degree of predictive capability than existing models. We also address the scalability issue of our intelligent traffic control decision support system by using a multi-agent based approach. The large network is divided into sub-networks, each of which has its own associated agent. Finally, our intelligent traffic control decision support system is applied to a number of road traffic case studies using the traffic network in Riyadh, in Saudi Arabia. The results obtained are promising and show that our intelligent traffic control decision support system can provide an effective support for real-time traffic control.

This diploma thesis is dedicated to the European Commission's project known under the name Single European Sky which pursues an objective to reorganize the current European airspace structure for the more efficient provision of air navigation services purposes. As an introduction, the author sets himself the goal to acquaint the reader with relevant international organizations in the field of civil aviation, introduce him in a general way into the provision of air navigation services' issues, including the situations when these services are being provided across the national boundaries. Following this general reading, the thesis fluently moves on to its main subject matter which is the Single European Sky initiative. An attention is focused on the state of European airspace before the initiative has been launched as well as the preparatory works which resulted in the adoption of the first SES legislative package. The following chapter presents the basic legal framework regulating the Single European Sky as amended by the adoption of the second legislative package. Principal part of the thesis is comprised in the next chapter dealing with the process of project's implementation. Within its framework, the reader's attention is turned, inter alia, to such crucial issues as the national supervisory...

This thesis project presents a traffic micro simulation study that investigates the state-of-the-art in traffic management "Green Light Optimal Speed Advisory (GLOSA)" for vehicles in a smart city. GLOSA utilizes infrastructure and vehicles communication through using current signal plan settings and updated vehicular information in order to influence the intersection approach speeds. The project involves simulations for a mixed traffic environment of conventional and automated vehicles both connected to the intersection control and guided by a speed advisory traffic management system. Among the project goals is to assess the effects on traffic performance when human drivers comply to the speed advice. The GLOSA management approach is also accessed for its potential to improve traffic efficiency in a full market penetration of connected automated vehicles with enhanced capabilities such as having shorter time head ways.

This thesis project investigates the state-of-the-art in traffic management "Green Light Optimal Speed Advisory (GLOSA)" for vehicles in a smart city. GLOSA utilizes infrastructure and vehicles communication through using current signal plan settings and updated vehicular information in order to influence the intersection approach speeds. The project involves traffic microscopic simulations for a mixed traffic environment of conventional and automated vehicles (AVs) both connected to the intersection control and guided by a speed advisory traffic management system. Among the project goals is to assess the effects on traffic performance when human drivers comply to the speed advice. The GLOSA management approach is accessed for its potential to improve traffic efficiency in a full market penetration of connected AVs with absolute compliance. The project also aims to determine the possible outcome resulting from enhancing the AVs capabilities such as implementing short time headways between vehicles in the future.  The best traffic performance results achieved by operating GLOSA goes for connected AVs with the lowest simulated time headway (0.3 sec). The waiting time reduction reaches 95% and trip delay lessens to 88 %.